Models for characterization, diagnosis, and treatment of human cancers using comparative canine-human multi-omics

Authors

• GJ Tawa
• A Leblanc
• D Gerhold
• M Breen
• C Thomas
• C Mazcko
• S Hoyt
• J Braisted
• GC Wicaksono
• S Huang
• L Ren
• C McNight
• K Wilson
• C Klumpp-Thomas
• D Holland
• X Zhang

Abstract

Most cancer modeling experiments are performed on rodents. While rodent models have been invaluable for investigating cancer mechanisms, they have not always been representative of the disease in humans. The use of companion animals to understand human tumor biology stems from decades of scientific observation that pet dogs spontaneously develop malignancies that share characteristics with human cancers. In this context, we developed an experimental/bioinformatics pipeline that generates and analyzes multi-disease, multi-species, next-generation sequencing data, identifies relevant disease genes common to the species analyzed and based on these common genes enumerates potential drug targets and therapeutic drug combinations. Validation of candidate genes is conducted using proteomics experiments to ensure that they express as proteins in tissue and existing drugs are identified that are known to modulate protein expression opposite to that exhibited in the disease. Drug combinations are derived such that each drug modulates different proteins to maximize synergy. Matrix screens are performed on canine cell lines and combinations that show significant efficacy and synergy are selected for further study in mouse PDX models with the eventual goal of translating these combinations into in vivo canine and human studies. Using this strategy, we found 68 protein targets and 6 drug combinations exhibiting synergy for canine osteosarcoma. Mouse PDX experiments at NCI are ongoing to evaluate these six drug combinations in vivo. This work exemplifies an approach that further establishes the relevance of canine to human cancer and provide opportunities to explore cancer mechanisms and treatments in both species.

Scientific Focus Area: Computational Biology

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